James M. Cheverud

Bio: James M. Cheverud is an academic researcher from Loyola University Chicago. The author has contributed to research in topics: Quantitative trait locus & Population. The author has an hindex of 80, co-authored 303 publications receiving 24497 citations. Previous affiliations of James M. Cheverud include Saint Louis University & Northwestern University.

The Collaborative Cross, a community resource for the genetic analysis of complex traits

Abstract: The goal of the Complex Trait Consortium is to promote the development of resources that can be used to understand, treat and ultimately prevent pervasive human diseases. Existing and proposed mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background are less effective for studying intact polygenic networks and interactions among genes, environments, pathogens and other factors. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way we approach human health and disease.

The road to modularity

Abstract: A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components. Modularity has emerged as a rallying point for research in developmental and evolutionary biology (and specifically evo-devo), as well as in molecular systems biology. Here we review the evidence for modularity and models about its origin. Although there is an emerging agreement that organisms have a modular organization, the main open problem is the question of whether modules arise through the action of natural selection or because of biased mutational mechanisms.

A comparison of genetic and phenotypic correlations

Abstract: Genetic variances and correlations lie at the center of quantitative evolutionary theory. They are often difficult to estimate, however, due to the large samples of related individuals that are required. I investigated the relationship of genetic- and phenotypic-correlation magnitudes and patterns in 41 pairs of matrices drawn from the literature in order to determine their degree of similarity and whether phenotypic parameters could be used in place of their genetic counterparts in situations where genetic variances and correlations cannot be precisely estimated. The analysis indicates that squared genetic correlations were on average much higher than squared phenotypic correlations and that genetic and phenotypic correlations had only broadly similar patterns. These results could be due either to biological causes or to imprecision of genetic-correlation estimates due to sampling error. When only those studies based on the largest sample sizes (effective sample size of 40 or more) were included, squared genetic-correlation estimates were only slightly greater than their phenotypic counterparts and the patterns of correlation were strikingly similar. Thus, much of the dissimilarity between phenotypic- and genetic-correlation estimates seems to be due to imprecise estimates of genetic correlations. Phenotypic correlations are likely to be fair estimates of their genetic counterparts in many situations. These further results also indicate that genetic and environmental causes of phenotypic variation tend to act on growth and development in a similar manner.

Phenotypic, genetic, and environmental morphological integration in the cranium.

Abstract: Modern theories of evolutionary change have stressed the unity of the genotype (Mayr, 1963, 1976; Lewontin, 1974; Wright, 1978, 1980). It is the total phenotype which is selected upon and the total genotype which evolves rather than individual phenotypic characters or genes. Individual phenotypic characters and genes only evolve within the larger context of the organism in which they occur. Therefore, organisms are the integrated functional units which evolve. This outlook on evolutionary change lends itself to a holistic, systems view of an organism (Waddington, 1957; Riedl, 1978; Gould and Lewontin, 1979). Mayr (1976) refers to the unity of the genotype because most phenotypic characters are the result of the collaboration of many structural and regulatory genes. Most traits of evolutionary importance are polygenic (Lewontin, 1974). Also, most genes are pleiotropic, affecting many different aspects of the phenotype (Wright, 1980). Mayr states, "It is no longer possible to regard the phenotype as a mosaic in which each part can be replaced without any effect on neighboring components . . . (also) the genotype is an organic whole with an internal harmony that, when exposed to a new selection pressure, will change . . . in a harmonious manner" (1976, p. 49). Both genotype and phenotype are systems composed of interacting components.

Evolutionary consequences of indirect genetic effects

Abstract: Indirect genetic effects (IGEs) are environmental influences on the phenotype of one individual that are due to the expression of genes in a different, conspecific, individual. Historically, work has focused on the influence of parents on offspring but recent advances have extended this perspective to interactions among other relatives and even unrelated individuals. IGEs lead to complicated pathways of inheritance, where environmental sources of variation can be transmitted across generations and therefore contribute to evolutionary change. The existence of IGEs alters the genotype-phenotype relationship, changing the evolutionary process in some dramatic and non-intuitive ways.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

TASSEL: software for association mapping of complex traits in diverse samples

Abstract: Summary: Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components. Availability: The TASSEL executable, user manual, example data sets and tutorial document are freely available at http://www. maizegenetics.net/tassel. The source code for TASSEL can be found at http://sourceforge.net/projects/tassel.

Modern Applied Statistics With S

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Testing for phylogenetic signal in comparative data: behavioral traits are more labile.

Abstract: The primary rationale for the use of phylogenetically based statistical methods is that phylogenetic signal, the tendency for related species to resemble each other, is ubiquitous. Whether this assertion is true for a given trait in a given lineage is an empirical question, but general tools for detecting and quantifying phylogenetic signal are inadequately developed. We present new methods for continuous-valued characters that can be implemented with either phylogenetically independent contrasts or generalized least-squares models. First, a simple randomization procedure allows one to test the null hypothesis of no pattern of similarity among relatives. The test demonstrates correct Type I error rate at a nominal α = 0.05 and good power (0.8) for simulated datasets with 20 or more species. Second, we derive a descriptive statistic, K, which allows valid comparisons of the amount of phylogenetic signal across traits and trees. Third, we provide two biologically motivated branch-length transformat...